Case studies

Steel Corp has a large production capacity but a shrinking steel market in Europe. Reaching growing markets like China and U.A.E will be important to sustaining and growing revenue but is tough due to higher transportation costs. In this case, users must identify and use logistics data; logistics customer segmentation and related cost analysis.

The COO for Suape Container Terminal, the largest deep–water port in Brazil's Northeast must consider a proposal presented by the users' council that calls for the establishment of a reservation scheme that minimizes the risk of docking delays. Under this proposal, ocean carriers, on the one hand, agree to pay a reservation fee that significantly increases revenue for Tecon Suape. On the other hand, they expect Tecon Suape to compensate them financially when a berth is not available upon vessel arrival. Tecon Suape's management team must evaluate that suggestion, as the team prepares to enter contractual negotiations with the users.

This case, along with its B case (UVA-QA-0865), is an effective vehicle for introducing students to the use of machine-learning techniques for classification. The specific context is predicting customer retention based on a wide range of customer attributes/features. The specific techniques could include (but are not limited to): regressions (linear and logistic), variable selection (forward/backward and stepwise), regularizations (e.g., LASSO), classification and regression trees (CART), random forests, graduate boosted trees (xgboost), neural networks, and support vector machines (SVM).

The case is suitable for an advanced data analysis (data science, machine learning, and artificial intelligence) class at all levels: upper-level business undergraduate, MBA, EMBA, as well as specialized graduate or undergraduate programs in analytics (e.g., masters of science in business analytics [MSBA] and masters of management analytics [MMA]) and/or in management (e.g., masters of science in management [MScM] and masters in management [MiM, MM]).

The teaching note for the case contains the pedagogy and the analyses, alongside the detailed explanations of the various techniques and their implementations in R (code provided in Exhibits and supplementary files). Python code, as well as the spreadsheet implementation in XLMiner, are available upon request.